- Tumors remain among the leading causes of mortality in the world, causing more than 9 million deaths per year.
- Colonoscopy remains the procedure par excellence to detect and prevent colorectal cancer.
- Now Artificial Intelligence can be added to a traditional colonoscopy to identify polyps that might otherwise go unnoticed.
Technology advances by leaps and bounds every day and its advantages can be seen in all areas. From entertainment to more specialized fields such as Medicine, they benefit from the new tools that are available today. Such is the case of the Artificial Intelligence because thanks to this alternative, greater precision can be obtained in a colonoscopy.
To that end, Dr. James East spends his time expertly examining people’s colons to look for and detect suspicious polyps that could eventually turn into cancer. This gastroenterologist at Mayo Clinic Healthcare in London says the ability to identify cancer risks and immediately eliminate them during a colonoscopy is one of the most rewarding parts of his chosen profession.
Evolution of technology for the benefit of patients
The colonoscopy It continues to be the procedure par excellence to detect and prevent colorectal cancer. But the procedure has its limitations. Some studies suggest that more than half of colon cancer cases detected by colonoscopy arise from lesions not seen on previous colonoscopies that the patient underwent.
Now, Mayo Clinic researchers are looking into how you can take advantage of the Artificial intelligence to increase the detection of polyps. In fact, gastroenterologists are incorporating Artificial Intelligence as a tool to improve care for a wide variety of conditions with the goal of discovering inaccurate signals earlier when diseases are most treatable.
In the case of colon cancerthe Artificial Intelligence system works together with the doctor in real time, analyzing the video colonoscopy material and drawing small red squares around the polyps that could go unnoticed.
“We all know facial recognition software. Instead of training the AI to recognize faces, we train it to recognize polyps.”
Artificial Intelligence can be added to a traditional colonoscopy to identify polyps that might otherwise go unnoticed.
More precise colon cancer care
Colon cancer is the second type of cancer with the highest mortality rate in the world. The past few decades have seen a steady increase in colorectal cancer rates in young people. While colonoscopy can be an effective screening tool, Dr. East says there is variability in how effective endoscopists are in detecting precancerous polyps.
Paradoxically, polyps are more difficult to discover in patients most in need of detection. Patients with inflammatory bowel diseases, such as Crohn’s disease or ulcerative colitis, are at increased risk of developing colorectal cancer. But the precancerous lesions that develop are usually flat or only slightly elevated, unlike the bulbous, mushroom-shaped polyps seen in people without inflammatory bowel disease.
“Most people with these types of cancer have regular colonoscopies and still don’t see the lesions because the tumors are so subtle,” says Nayantara Coelho-Prabhu, a gastroenterologist at Mayo Clinic in Rochester, Minnesota.
That data bank provides what Dr. Coelho-Prabhu and other experts call “real data,” the real-world observations and readings that were used to train and test the AI algorithms. Her team is commenting on data from a subset of 1,000 patients and reviewing one video colonoscopy after another to mark lesions in every frame, from every angle.
After annotating these images, the researchers will input that data into a computer to generate the kinds of AI “machine learning” algorithms that enable the machine to learn to recognize specific inflammatory bowel disease polyps on its own.
Dr. Coelho-Prabhu, Dr. Cadman Leggett, a gastroenterologist, and their colleagues are developing an innovative digital platform for endoscopy that will film all internal procedures, link them to medical records, and then re-integrate whatever procedures are relevant to Artificial Intelligence.
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